Assistant Manager, Data Scientist at I&M Bank

Job Purpose:




• The Data Scientist will develop and implement data science models and analytical solutions that address business challenges and uncover growth opportunities.


• The Data Scientist applies statistical techniques, machine learning algorithms, and data science methods to derive insights and support evidence-based decisions.



Roles & Responsibilities


Key Responsibilities:


Strategic:




• Stay abreast of new ML/AI methods and propose applicable solutions.


• Align model development efforts with broader team priorities and ethics guidelines.


• Provide input into project scoping and business value estimation.



Initiatives:




• Design, develop, and deploy machine learning models.


• Clean, prepare, and engineer features from structured and unstructured data.


• Collaborate with stakeholders to ensure models address real business problems.


• Present insights and model results in understandable formats.



Operational:




• Write production-ready code and maintain model pipelines.


• Conduct peer reviews and contribute to code repositories.


• Document assumptions, methodologies, and model limitations.


• Monitor model performance and recalibrate as needed.


• Contribute to internal knowledge sharing and continuous learning efforts.



Key Responsibilities:


Problem Scoping:




• Work with business teams to define problems.


• Frame use cases into model-ready formats.


• Perform initial feasibility assessments.



Data Preparation:




• Extract, clean, and prepare data.


• Conduct exploratory data analysis.


• Engineer relevant features.



Model Development:




• Build machine learning/statistical models.


• Train and tune models using appropriate metrics.


• Evaluate performance and robustness.



Insight Communication:




• Visualize and interpret results.


• Translate findings into business language.


• Support adoption and stakeholder understanding.



Deployment and Monitoring:




• Package models for deployment (API, batch, etc.)


• Monitor performance and drift.


• Maintain logs and feedback loops.



Collaboration and Learning:




• Participate in peer reviews and team learning.


• Stay updated on new tools/techniques.


• Contribute to knowledge sharing.



Academic Qualifications:




• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.



Professional Qualifications / Membership to professional bodies/ Publication:




• Practical ML certifications (e.g. Coursera, Udacity, DataCamp).


• Python, R, or SQL proficiency certifications.


• Cloud ML certifications (AWS, Azure, GCP) are a plus.



Work Experience Required:




• 2–5 years in data science, machine learning, or predictive modeling roles.



Key Competencies:




• Statistical & Machine Learning Knowledge: applies predictive modeling, classification, clustering, and other techniques to solve real problems.


• Programming Proficiency: proficient in Python, R, and SQL, with hands-on experience using data science libraries and frameworks.


• Data Wrangling & Feature Engineering: transforms raw data into meaningful inputs for models through cleaning, joining, and deriving features.


• Curiosity & Innovation: constantly seeks new methods, algorithms, and technologies to improve performance


• Model Deployment & Monitoring: Familiar with putting models into production, versioning, and tracking performance over time.


• Communication of Insights: translates complex analysis into clear, actionable insights tailored to the business audience.


• Collaboration & Teamwork: works effectively with analysts, engineers, and business teams to drive outcomes.


• Adaptability: quickly learns new tools and adjusts to changing priorities or technologies.